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Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems (2210.17319v2)
Published 31 Oct 2022 in physics.flu-dyn and cs.LG
Abstract: In the absence of high-resolution samples, super-resolution of sparse observations on dynamical systems is a challenging problem with wide-reaching applications in experimental settings. We showcase the application of physics-informed convolutional neural networks for super-resolution of sparse observations on grids. Results are shown for the chaotic-turbulent Kolmogorov flow, demonstrating the potential of this method for resolving finer scales of turbulence when compared with classic interpolation methods, and thus effectively reconstructing missing physics.